{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正态分布代表了宇宙中大多数情况的运转状态。大量的随机变量被证明是正态分布的。\n",
    "\n",
    "若随机变量X服从一个数学期望为μ、方差为σ^2的正态分布，记为N(μ，σ^2)。其概率密度函数为正态分布的期望值μ决定了其位置，其标准差σ决定了分布的幅度。当μ = 0,σ = 1时的正态分布是标准正态分布。\n",
    "***\n",
    "\n",
    "## 公式\n",
    "$$ f(x|\\mu, \\sigma) = \\frac{1}{\\sqrt{2\\pi\\sigma^{2}}}e^{-\\frac{(x-\\mu)^{2}}{2\\sigma^{2}}}$$\n",
    "- $\\mu$ 是均值 \n",
    "- $\\sigma$ 是标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "datas = np.linspace(-4, 4, 100)\n",
    "# PDF\n",
    "plt.plot(datas, stats.norm.pdf(datas))\n",
    "# CDF\n",
    "plt.plot(datas, stats.norm.cdf(datas))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
